Mapping of m6A and Its Regulatory Targets in Prostate Cancer Reveals a METTL3-Low Induction of Therapy Resistance

Cotter, Kellie A.; Gallon, John; Uebersax, Nadine; Rubin, P.; Meyer, Kate D.; Piscuoglio, Salvatore; Jaffrey, Samie R.; Rubin, Mark A.

doi:10.1158/1541-7786.mcr-21-0014

Cited by 29 publications

(39 citation statements)

References 74 publications

(104 reference statements)

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“…This concept has been recently validated in murine cancer models ( 78 ). Fifth, a negative correlation of AR activity with m 6 A activity is noticed, and together with a recent report that AR is not a direct target of METTL3 ( 79 ), we propose AR and m 6 A axis as independent contributors in driving PCa progression. Sixth, m 6 Avalue, a weighted score established from 87 key m 6 A-associated genes, well separates indolent pri-PCa from aggressive ones, with the m 6 Avalue high group being more stem-like, proliferative and nonfibrotic.…”

Section: Discussionsupporting

confidence: 81%

A m6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response

Zou

Feng

et al. 2022

NAR Cancer

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The molecular mechanisms underpinning prostate cancer (PCa) progression are incompletely understood, and precise stratification of aggressive primary PCa (pri-PCa) from indolent ones poses a major clinical challenge. Here, we comprehensively dissect, genomically and transcriptomically, the m6A (N6-methyladenosine) pathway as a whole in PCa. Expression, but not the genomic alteration, repertoire of the full set of 24 m6A regulators at the population level successfully stratifies pri-PCa into three m6A clusters with distinct molecular and clinical features. These three m6A modification patterns closely correlate with androgen receptor signaling, stemness, proliferation and tumor immunogenicity of cancer cells, and stroma activity and immune landscape of tumor microenvironment (TME). We observe a discrepancy between a potentially higher neoantigen production and a deficiency in antigen presentation processes in aggressive PCa, offering insights into the failure of immunotherapy. Identification of PCa-specific m6A phenotype-associated genes provides a basis for construction of m6Avalue to measure m6A methylation patterns in individual patients. Tumors with lower m6Avalue are relatively indolent with abundant immune cell infiltration and stroma activity. Interestingly, m6Avalue separates PCa TME into fibrotic and nonfibrotic phenotypes (instead of previously reported immune-proficient or -desert phenotypes in other cancer types). Significantly, m6Avalue can be used to predict drug response and clinical immunotherapy efficacy in both castration-resistant PCa and other cancer types. Therefore, our study establishes m6A methylation modification pattern as a determinant in PCa progression via impacting cancer cell aggressiveness and TME remodeling.

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Section: Discussionsupporting

confidence: 81%

A m6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response

Zou

Feng

et al. 2022

NAR Cancer

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“…YTHDF2, a reader of m6A modification, synergistically induces PCa progression with METTL3 by regulating AKT phosphorylation [ 15 ]. However, low levels of METTL3 were also found to lead to advanced metastatic PCa that is resistant to androgen receptor antagonists [ 35 ]. These controversial results reveal the complexity of m6A regulators and individual heterogeneity of m6A regulators in PCa, and various m6A regulators may form a complex network structure and interact with each other to affect PCa progression.…”

Section: Discussionmentioning

confidence: 99%

Construction of a risk prediction model using m6A RNA methylation regulators in prostate cancer: comprehensive bioinformatic analysis and histological validation

2022

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Background Epigenetic reprogramming reportedly has a crucial role in prostate cancer (PCa) progression. RNA modification is a hot topic in epigenetics, and N6-methyladenosine (m6A) accounts for approximately 60% of RNA chemical modifications. The aim of this study was to evaluate the m6A modification patterns in PCa patients and construct a risk prediction model using m6A RNA regulators. Materials and methods Analyses were based on the levels of 25 m6A regulators in The Cancer Genome Atlas (TCGA). Differentially expressed gene (DEG) and survival analyses were performed according to TCGA-PRAD clinicopathologic and follow-up information. To detect the influences of m6A regulators and their DEGs, consensus clustering analysis was performed, and tumor mutational burden (TMB) estimation and tumor microenvironment (TME) cell infiltration were assessed. mRNA levels of representative genes were verified using clinical PCa data. Results Diverse expression patterns of m6A regulators between tumor and normal (TN) tissues were detected regarding Gleason score (GS), pathological T stage (pT), TP53 mutation, and survival comparisons, with HNRNPA2B1 and IGFBP3 being intersecting genes. HNRNPA2B1 was upregulated in advanced stages (GS > 7, pT3, HR > 1, and TP53 mutation), as verified using clinical PCa tissue. Three distinct m6A modification patterns were identified through consensus clustering analysis, but no significant difference was found among these groups in recurrence-free survival (RFS) analysis. Six DEGs of m6A clusters (m6Aclusters) were screened through univariate Cox regression analysis. MMAB and PAIAP2 were intersecting genes for the five clinical factors. MMAB, which was upregulated in PCa compared with TN, was verified using clinical PCa samples. Three distinct subgroups were established according to the 6 DEGs. Cluster A involved the most advanced stages and had the poorest RFS. The m6A score (m6Ascore) was calculated based on the 6 genes, and the low m6Ascore group showed poor RFS with a negative association with infiltration for 16 of 23 immune-related cells. Conclusion We screened DEGs of m6Aclusters and identified 6 genes (BAIAP2, TEX264, MMAB, JAGN1, TIMM8AP1, and IMP3), with which we constructed a highly predictive model with prognostic value by dividing TCGA-PRAD into three distinct subgroups and performing m6Ascore analysis. This study helps to elucidate the integral effects of m6A modification patterns on PCa progression.

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“…Finally, GO analysis showed that ELAVL1-immunoprecipitated mRNAs were associated with RNA metabolism functions, including RNA decay, translation, splicing, and nuclear export ( Figure 5D ). Previous studies have shown that PCa development is associated with RNA metabolism due to m6A imbalance ( 49 – 51 ). Therefore, we speculate that ELAVL1 in PCa may be involved in tumor development by regulating the RNA metabolism of m6A regulators.…”

Section: Resultsmentioning

confidence: 99%

ELAVL1 promotes prostate cancer progression by interacting with other m6A regulators

Cai

Bai

et al. 2022

Front. Oncol.

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N6-Methyladenosine (m6A) imbalance is an important factor in the occurrence and development of prostate cancer (PCa). Many m6A regulators have been found to be significantly dysregulated in PCa. ELAVL1 is an m6A binding protein that can promote the occurrence and development of tumors in an m6A-dependent manner. In this study, we found that most m6A regulators were significantly dysregulated in PCa, and some m6A regulators were associated with the progression-free interval. Mutations and copy number variations of these m6A regulators can alter their expression. However, ELAVL1 mutations were not found in PCa. Nevertheless, ELAVL1 upregulation was closely related to PCa proliferation. High ELAVL1 expression was also related to RNA metabolism. Further experiments showed that ELAVL1 interacted with other m6A regulators and that several m6A regulatory mRNAs have m6A sites that can be recognized by ELAVL1. Additionally, protein–protein interactions occur between ELAVL1 and other m6A regulators. Finally, we found that the dysregulation of ELAVL1 expression occurred in almost all tumors, and interactions between ELAVL1 and other m6A regulators also existed in almost all tumors. In summary, ELAVL1 is an important molecule in the development of PCa, and its interactions with other m6A regulators may play important roles in PCa progression.

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Mapping of m6A and Its Regulatory Targets in Prostate Cancer Reveals a METTL3-Low Induction of Therapy Resistance

Cited by 29 publications

References 74 publications

A m6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response

A m6Avalue predictive of prostate cancer stemness, tumor immune landscape and immunotherapy response

Construction of a risk prediction model using m6A RNA methylation regulators in prostate cancer: comprehensive bioinformatic analysis and histological validation

ELAVL1 promotes prostate cancer progression by interacting with other m6A regulators

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